Many parameters affect the quality and usefulness of an image of a scene acquired by a camera. For example, parameters configured to control exposure time affect motion blur, parameters configured to control f/number affect depth-of-field, and so forth. In many cameras, all or some of these parameters can be controlled and are conveniently referred to herein as image-acquisition settings.
Methods for controlling exposure and focus parameters are well known in both film-based and electronic cameras. However, the level of intelligence in these systems is limited by resource and time constraints in the camera. In many cases, knowing the type of scene being acquired can lead easily to improved selection of image-acquisition parameters. For example, knowing a scene is a portrait allows the camera to select a wider aperture to minimize depth-of-field. Knowing a scene is a sports/action scene allows the camera to automatically limit exposure time to control motion blur and adjust gain (exposure index) and aperture accordingly. Knowing the scene is a sunset suggests that the color balance will be shifted from the norm and that high saturation is likely to be desired. Knowing a scene is a snow scene indicates that a special mapping of input brightness to output values is desired. Because this knowledge is useful in guiding simple exposure control systems, many film, video, and digital still cameras include a number of scene modes that can be selected by the user. These scene modes are essentially collections of image-acquisition settings, which direct the camera to optimize parameters given the user's selection of scene type.
The use of scene modes is limited in several ways. One limitation is that the user must select a scene mode for it to be effective, which is often inconvenient, and shifts the burden of scene determination from the image-acquisition device to the user. The average user generally understands little of the utility and usage of the scene modes.
A second limitation is that scene modes tend to oversimplify the possible kinds of scenes being acquired. For example, a common scene mode is “portrait”, which is optimized for capturing images of people. Another common scene mode is “snow”, which is optimized to acquire a subject against a background of snow with different parameters. If a user wishes to acquire a portrait against a snowy background, the user must choose either portrait or snow, but the user cannot combine aspects of each. Many other combinations exist, and creating scene modes for the varying combinations is cumbersome at best. In another example, a backlit scene can be very much like a scene with a snowy background, in that subject matter is surrounded by background with a higher brightness. Few users are likely to understand the concept of a backlit scene and realize it has crucial similarity to a “snow” scene. A camera developer wishing to help users with backlit scenes will probably have to add a scene mode for backlit scenes, even though it may be identical to the snow scene mode.
Both of these scenarios illustrate the problems of describing photographic scenes in way accessible to a casual user. The number of scene modes required expands greatly and becomes difficult to navigate. The proliferation of scene modes ends up exacerbating the problem that many users find scene modes excessively complex.
Attempts to automate the selection of a scene mode have been made, for example, in United States Patent Application Publication No. 2003/0007076 by Noriyuki Okisu et al. and U.S. Pat. No. 6,301,440, to Rudolf M. Bolle et al. A limitation on such automated methods is that they tend to be computationally intensive relative to the simpler methods. In this regard, cameras tend to be relatively limited in computing resources, in order to reduce cost, cut energy drain, and the like. Consequently, a noticeable lag between shutter trip and image acquisition occurs in some cameras. Such lag is highly undesirable when a subject to be photographed is in motion. One solution to the problem of lag is avoidance of highly time consuming computations, which leads us back again to the also-undesirable use of fewer, manually selected modes with associated image-acquisition settings.
Accordingly, a need in the art exists for improved solutions for determining image-acquisition settings in a computationally-sensitive environment.